The Tech Trek

Michael White, Co founder and CEO of Multiply, joins the show to talk about the path from engineering leadership to the CEO seat, and what it really takes to build in a high trust, high complexity market. If you are thinking about founder readiness, leadership growth, or where AI creates real value in fintech, this episode gets into the parts that matter.Michael shares how early entrepreneurial instincts showed up long before Multiply, what changed as he moved from builder to company leader, and why some of the most important skills in leadership have less to do with code and more to do with communication, conviction, and influence. He also breaks down how Multiply is using AI to improve the mortgage experience without removing the human element people still need in a major financial decision. In this episode:• The mindset shift from engineer to CEO• Why leadership becomes a form of sales• How founder timing can be an advantage, not a delay• Where AI fits in the mortgage process, and where it does not• Why startups can move faster than legacy players in AI adoption Timestamped highlights00:43 What Multiply is building, and why an AI native mortgage company sees a better path to homeownership01:47 The childhood business story that hinted at an entrepreneurial future06:20 What changed in the move from engineering leadership to founder and CEO08:45 Why so much of leadership comes down to influence, alignment, and selling the vision17:19 Why mortgages are such a strong use case for AI, and why the back office is the real opportunity22:39 The startup advantage in AI, speed, focus, and freedom from legacy systems Follow the show for more conversations with founders, operators, and technology leaders building what comes next.

Susan Liu, Partner at Uncork Capital, joins Amir to break down what actually matters when backing early stage AI companies. From founder market fit to product wedge to the reality of churn, this conversation gets past the hype and into how strong companies separate themselves in a crowded market.If you are building, funding, or evaluating AI startups, this episode gives you a sharper lens on where the market is heading, what Series A investors now expect, and why real ROI is becoming the line between momentum and fallout.What stood out• The best early stage founders usually have earned insight, meaning they have lived the problem before building the solution• In crowded AI markets, the goal is not to be interesting, it is to become one of the few companies that actually wins• AI buyers still care about the same core question, does this drive revenue or cut cost in a measurable way• The Series A bar has moved up fast, and strong growth alone is not enough if retention is weak• Some of today’s biggest AI winners may still face painful churn if they are not truly essential to the customerTimestamped Highlights00:37 Susan breaks down how Uncork Capital invests at seed and what it takes to get real conviction early02:00 The three-part framework she uses to evaluate companies, team, market, and product wedge with traction09:42 Why crowded AI markets are not necessarily a red flag, and how winners still pull away from the pack17:04 The ROI test every AI startup has to pass if it wants to survive renewals19:05 Susan’s honest take on 2026, cautious optimism, bigger impact, and a likely wave of churn24:33 What founders need now to raise a strong Series A in a market where the bar is higher than everOne line that stuck“If you cannot prove one of these two, it is going to be a tough sell. Companies are not going to renew.”Practical takeaways for operators and founders• If your product cannot clearly tie to revenue growth or cost savings, buyers will eventually cut it• Founder credibility matters more when the market gets noisy, especially in AI• A compelling wedge wins attention, but retention is what keeps the story alive• Happy customers who will speak for you can be one of the strongest assets in a fundraiseStay connectedIf this episode gave you a better lens on AI startups, venture, and what actually drives durable value, follow the show, share it with a founder or operator in your network, and keep up with Amir on LinkedIn for more conversations like this.

What happens to e commerce when AI agents start shopping instead of humans?Maju Kuruvilla, Founder and CEO of Spangle, joins the show to unpack a shift most companies are not prepared for. If AI agents become buyers, the entire digital shopping experience must change. Websites today are designed for human psychology, not machines making decisions.In this conversation, Maju explains why context is becoming the most important layer in commerce. From marketing clicks to storefront visits, most companies lose the context that originally inspired a purchase. The future belongs to systems that can capture, carry, and act on that context across every channel. The discussion explores agent driven shopping, the limits of traditional customer data systems, and how AI can reshape both online and physical retail experiences.Key Takeaways• Context matters more than identity. Knowing what someone is trying to do right now is often more valuable than knowing who they are.• Most e commerce experiences reset the customer journey. When someone clicks from an ad to a site, the original inspiration is usually lost.• AI agents will shop differently than humans. They are not influenced by visual design or marketing psychology the same way people are.• Commerce will not become fully agent driven. Instead, brands must design experiences that work for humans, agents, and hybrid interactions.• Physical retail may benefit the most from AI driven context because stores can blend digital signals with real world behavior.Timestamped Highlights00:00 Why the next generation of e commerce will be built for AI agents, not just human shoppers.02:08 The hidden problem in online shopping today. Most websites lose the context that brought the customer there.06:11 Buyer agents and seller agents. How commerce may evolve into AI systems negotiating purchases.11:38 Why a simple request like “buy a red sweater” is actually a complex problem of interpretation and context.16:30 How AI could transform physical stores through dynamic recommendations and real time shopping guidance.22:30 Why collecting endless customer data might be the wrong approach to personalization.27:59 The future of autonomous shopping and why personal AI agents may eventually handle everyday purchases.A Moment That Sticks“Context is what matters. The fact that I bought a TV before is interesting, but not important. What matters is what I am trying to do right now.”Practical Insight for BuildersIf you are building AI driven commerce tools, start with the product layer.According to Maju, the foundation is making your product catalog intelligent. AI systems need rich product understanding so they can match intent with inventory. Once the catalog becomes machine readable and context aware, everything else becomes easier to automate.Call to ActionIf you enjoyed this conversation, follow the show and share this episode with someone working at the intersection of AI, commerce, or product development.New conversations every week with the builders shaping the future of technology.

Most people never think about the technology behind construction equipment rentals. But behind every crane, excavator, and lift is an industry still running on paper, spreadsheets, and manual workflows.In this episode, Andy Feis, CEO and Co-Founder of Renterra, joins Amir to explain how a hundred billion dollar equipment rental market is finally entering the modern software era. The conversation explores how operational software, telematics data, and AI are reshaping one of the most overlooked parts of the industrial economy. Andy shares how rental companies manage fleets of expensive machines, why legacy workflows still dominate the industry, and how platforms like Renterra are bringing cloud software and automation to a sector that has largely been left behind by the tech revolution.This episode also explores the intersection of operational data, AI automation, and real world infrastructure. From fleet optimization to automated maintenance insights, the future of equipment rental may look very different than it does today.Key Takeaways• The equipment rental industry is a massive but overlooked market where over half of construction equipment is rented rather than owned.• Many rental businesses still run critical operations using pen and paper, manual inspections, and outdated spreadsheets.• Operational software is the first step toward modernization, helping companies manage inventory, dispatch, pricing, and maintenance.• Telematics data from machines unlocks powerful insights around maintenance timing, asset valuation, and fleet utilization.• AI will not replace the physical work in industrial sectors, but it can automate low value operational tasks and dramatically improve decision making.Timestamped Highlights00:00 Introducing the hidden technology opportunity inside the equipment rental industry02:00 Why many rental companies still rely on paper, binders, and manual equipment checks06:20 How Andy Feis discovered a massive opportunity inside industrial operations09:00The low hanging fruit in modernizing equipment rental workflows11:14 What kind of data heavy machines actually generate and how it can be used13:03 Where AI actually helps blue collar industries today20:18 The roadmap for modernizing the industry and what comes nextA Moment That Stuck“The industrial sector is an enormous part of the economy, but it has been one of the last places to feel the impact of the broader tech revolution.” Pro TipsIf you are building technology for legacy industries, start with operational efficiency before advanced analytics.Modernization works best when it removes friction from existing workflows. Once companies see time savings and operational improvements, they become far more open to deeper data and AI driven insights.Call to ActionIf you enjoy conversations about technology transforming real world industries, follow the show and share this episode with someone building in construction, logistics, or industrial software.

Luke Fischer, cofounder and CEO of SkyFi, breaks down how earth intelligence is becoming searchable, and why that changes decision making across defense, energy, logistics, and agriculture.You will hear how his path from Army special operations aviation to Head of Flight Ops at Uber shaped SkyFi’s product mindset, plus a practical look at what geospatial imagery and analytics can actually answer today.Key Takeaways• Networks are not nice to have, they are the fastest path to trust, hiring, and deals, especially in government and high stakes markets• SkyFi’s core unlock is access, making it possible to task satellites, pull history, and ask questions of the data, not just look at images• Going commercial first can create a faster iteration loop, then government adoption follows once the product is battle tested• The real product future is answers, not imagery, using natural language queries that return decisions grade insight• Privacy is not only about resolution, it is also about who can buy data, screening, and compliance, because access is the real leverage pointTimestamped Highlights00:47 Earth intelligence in plain English, task satellites, pull decades of history, ask questions like vessel detection or soil moisture06:32 Why veteran resumes miss the mark, and how to translate leadership without goofy title inflation10:44 The origin story, a broken buying experience in satellite imagery turns into SkyFi’s wedge16:42 Selling into government, people game first, acquisition reality, and why patience is a feature19:46 Use cases you will not expect, livestock behavior, barge counting, palm heights, mineral detection, and more28:10 Where this is headed, ask a question about the world, get an answer, then move toward proactive intelligenceA line worth repeating“Startups are the same thing, you are finding the right people with the right traits to solve these undefined problems in being comfortable with risk.”Practical moves you can stealIf you are hiring, screen for comfort with ambiguity, not just pedigree, undefined problems are the job in high growth workIf you are selling, build your network before you need it, warm paths beat cold volume every timeIf you are building product, shorten the feedback loop, commercial iteration can harden the product before slower cycle buyers adoptCall to ActionIf this episode sparked ideas for how data, defense, or AI driven analytics will reshape markets, follow the show and turn on notifications so you do not miss the next one. Also share it with one operator who makes high stakes decisions and would appreciate a clearer view of what is happening on the ground.

Anish Agarwal went from MIT PhD researcher to founding Traversal, an AI company building intelligent site reliability engineering agents for the enterprise. In this episode, he breaks down what it actually takes to lead an AI first company when your entire career was built inside a lab.This is not your typical founder story. Anish never planned to start a company. He was on track to be a professor at Columbia when generative AI hit and rewired his trajectory. Now he is two years into the CEO seat, recruiting top talent away from high paying jobs, and building a product at the intersection of causal machine learning and agentic systems.We get into the mechanics of that transition. How do you go from publishing papers to pitching investors? What does storytelling look like when you are convincing engineers to leave comfortable roles and bet on your vision? And what happens when you start a company without even having an idea?Anish also tackles a question the AI space is wrestling with right now. Is a PhD becoming table stakes for building an AI first company? His answer is more nuanced than you might expect. It is not the degree. It is the training. Reading the landscape, navigating uncertainty, and evaluating models with scientific rigor. Those skills separate builders from everyone else.Key TakeawaysThe best AI founders are not chasing credentials. They are leveraging research instincts to read where models and architectures are heading, and that foresight creates real competitive edges.Starting a company without an idea is not reckless if you have the right co founders. Anish and his team showed up to a WeWork every day and treated idea exploration like a research problem until the right opportunity clicked.Storytelling is the most underrated leadership skill in technical companies. Whether you are recruiting, raising capital, or explaining your product to nontechnical buyers, packaging complexity into a clear narrative is what moves people.Every decision as a founder is a bet, including the decision to do nothing. Viewing inaction as a strategic choice changes how you prioritize and how fast you move.As AI writes more code, someone has to make sure it works in production. That gap between code generation and reliability is where Traversal lives, and it is only getting wider.Timestamped Highlights(00:36) What Traversal does and why AI powered site reliability engineering is a massive unsolved problem in enterprise software(02:00) The moment generative AI changed everything and why Anish walked away from a career he loved(08:43) How Traversal found its problem without starting with an idea, and the co founder dynamic that made it work(14:29) The real advantage of a PhD in AI and why it has nothing to do with the letters after your name(19:49) Advice for PhDs entering the job market on how to position research experience so hiring managers actually get it(20:29) Two years into the CEO role, what Anish wishes he had known and the skills that matter most for early stage foundersWords That Stuck"If AI is writing your code, it has to fix it too. And right now it is only writing the code."Founder PlaybookPick a problem that sustains you for decades. Anish looks for problems that keep getting more complicated because that is where long term value compounds. If the problem has a ceiling, your company does too.Treat recruiting like a core product skill. Painting a compelling picture of the mission is not a nice to have. It is the engine that pulls exceptional talent away from safe, well paying jobs.Think of everything as a series of bets. Fundraising, hiring, product decisions, even waiting. Inaction is a bet too. Once you see it that way, you stop overthinking and start moving with intention.Subscribe to The Tech Trek wherever you listen. If this one hit home, share it with a founder or tech leader navigating their own leap. Follow the show on LinkedIn for more.

Harry Gestetner built a creator economy platform in college, sold it, and walked away. Then he did the one thing nobody expected. He jumped back in and started building hardware.In this episode, the founder and CEO of Orion (a sleep tech company making smart mattress covers) sits down to talk about what really happens after an exit, why most founders can't stay away from building, and what changes when you go from software to physical products.Harry shares what surprised him about the acquisition process, how he thinks about evaluating new startup ideas, and why he believes hardware is "life on hard mode." He also gets into the mental side of founding, from managing stress to staying sharp when everything feels uncertain.What You'll Walk Away WithGoing through an exit sounds like the finish line, but Harry explains why it's actually a reset. You trade ownership and freedom for financial security, and at some point, most founders start craving the creative control they gave up.Not every idea deserves your time. Harry talks about running new concepts through a "disqualification period" where you actively try to poke holes before committing. The ones that survive that process are worth going all in on.Hardware changes the game. Software lets you pivot fast. Hardware gives you 18 month product cycles, inventory headaches, and supply chain complexity. Conviction has to be higher before you start.The best startup ideas come from problems you and your friends actually have. If enough people share that problem, you've got a market.Knowledge compounds across startups. Harry compares the founder journey to an elastic band. Once you've been stretched, you never go back to your original form. Every challenge you survive makes the next one more manageable.Timestamped Highlights[00:34] What Orion actually does and how it makes six hours of sleep feel like ten[03:01] The emotional arc of an exit that nobody talks about, from relief to restlessness[05:34] How Harry evaluates startup ideas and why he uses a disqualification process[09:30] Why building hardware is "life on hard mode" and what made him take it on anyway[10:39] The elastic band theory of founder growth and why learning compounds over time[15:49] His advice for early career founders: pick one thing and go all inWords That Stuck"As a founder, you're sort of like an elastic band. The more you get stretched, you never go back to the original form."Tactical TakeawaysRun every new idea through a disqualification period. Actively look for reasons it won't work before you commit. The ideas that survive that scrutiny are the ones worth building.Build around problems you personally experience. If your friends share the same frustration, there's a good chance others do too. That's your market signal.If you're going to start something, go all in. Stop hedging across multiple projects. Pick one idea and dedicate yourself to it completely until it works.Keep Up With The ShowIf this episode hit home, share it with a founder or someone thinking about taking the leap. Subscribe wherever you listen so you never miss an episode. And connect with us on LinkedIn for more conversations like this one.

Behnam Bastani, CEO and cofounder of OpenInfer, breaks down why the last two years of AI feel explosive, and why the next wave is not chat, it is action at the edge.We get into always on inference, what actually forces compute to move closer to the data, and the missing layer that makes edge AI scale: the Android like infrastructure that lets devices collaborate instead of living in silos.Key takeaways• The hype spike is real, but the runway is decades, it took compute, sensors, and communication protocols maturing over generations to unlock this moment• AI is shifting from conversational to actionable, which means continuous, always on inference becomes the norm• Edge wins when cost, reliability, and data sovereignty matter, cloud and edge will coexist, but the workload placement changes• The biggest bottleneck is not just silicon, it is the infrastructure layer that makes building and deploying across devices easy, plus a shared fabric so devices can cooperate• Adoption is as much a human story as a technical one, this shift lands faster and broader than previous tech transitions, so anxiety is predictable and needs real attentionTimestamped highlights00:38 OpenInfer’s mission, intelligence on every physical surface, and why collaboration matters02:07 Electricity as the earlier revolution, intelligence as the next kind of power, and the control problem05:54 Where we really are on the maturity curve, early products are here, mass adoption and safety take time08:31 When the device boundary disappears, it stops being you versus the agent, it becomes one system11:04 Always on inference, and the three forces pushing compute to the edge: cost, reliability, data sovereignty14:40 The Android moment for edge AI, why the operating system layer unlocks developers, apps, and adoptionA line worth replayingThose are going to be the three pillars that really enforces that edge and cloud are going to live together.Pro tips for builders• If your product needs real time decisions, design for intermittent networks from day one, reliability is not optional• Treat data sovereignty as a product feature, not a compliance afterthought, it is becoming the moat• Push for interoperability early, the fabric that lets devices share the right data is what makes edge feel seamlessCall to actionIf this episode helped you rethink where AI should run and what it takes to ship it in the real world, follow the show and share it with one builder who is working on edge, robotics, devices, or applied AI.

Building data capability from zero is not a tooling problem, it is a trust and prioritization problem. In this episode, Laura Guerin, Head of Data and Data Science at Bevi, breaks down how she goes from blank slate to real business impact, without getting trapped in endless plumbing or endless meetings. Laura shares how she runs an early listening tour, prototypes value before asking for bigger investment, and decides when to hire scrappy generalists versus specialists. We also get practical on AI, where it helps, where it is unnecessary, and why quality data and a clean semantic layer still decide whether anything works.Key takeaways• Start with business priorities, then map data work to the actions and outcomes leaders actually care about• Prototype the end deliverable fast, even if the backend is duct tape at first, then scale after stakeholders see value• Use cases first for AI, most problems do not need AI, but the right problems can see real acceleration• Early teams win with adaptable generalists who can wear multiple hats across data, analytics, and data science• Trust is a shared responsibility, build reliability, then create a culture where users flag weirdness quicklyTimestamped highlights00:44 Bevy explained, smart bottle less dispensers and why the business context matters for data priorities02:01 The listening tour playbook, exec alignment, stakeholder map, and using AI to synthesize themes into a SWOT04:00 The MVP reality, manual prototypes to prove value, then the conversation about scalable pipelines06:33 AI without the hype, use cases, when AI is not needed, and two examples with clear business impact09:22 Hiring from zero, why generalists first, the data analytics data science spectrum, and the personality traits that matter14:21 Self service reimagined, Slack as the interface, semantic layer and permissions, and how to keep a single source of truth20:19 Keeping trust when things break, checks and balances plus a shared responsibility model22:39 Making innovation real, baking it into expectations so the team has time to learn and test new approachesA line worth stealingData on its own is not typically a priority. It is more about the action or the impact that comes out of the data.Pro tips• Run a structured listening tour early, capture themes, then pick two or three priorities you can deliver quickly• Show the business an MVP output first, then use that proof to justify the unglamorous backend work• Treat AI like any other tool, define the problem, validate the use case, then confirm the data quality inputsCall to actionIf you are building analytics, data products, or AI inside a growing company, follow the show and subscribe so you do not miss the next operator level conversation. Share this episode with one leader who is asking for data outcomes but has not funded the foundation yet.

Riya Grover, CEO and co founder of Sequence, breaks down what “good CEO” actually looks like when the job is messy, fast, and high stakes. This is a practical conversation about building excellence through people, clarity, and direction, not through heroics or micromanagement. Riya runs a revenue automation platform for finance teams, helping companies automate order to cash, billing, invoicing, accounts receivable, and revenue recognition. From that seat, she shares a founder level view on leadership that is direct, repeatable, and built for real operating constraints.Key takeaways• The CEO’s highest leverage job is building the bench, your company becomes the team you assemble• High performance culture comes from a clear bar, fast decisions when it is not met, and leaders who own outcomes• Great teams do not need more policies, they need context, goals, trade offs, and clarity• Separate reversible decisions from irreversible ones, move fast on two way doors, slow down on one way doors• Hiring signal to watch, motivation and hunger for the stretch challenge often beats the “done it before” resumeTimestamped highlights00:32 What Sequence does, why order to cash is still painfully manual01:48 The CEO role is less about functions, more about direction and execution03:23 Excellence starts with talent density, do not compromise on the bar06:10 Why companies win, direction plus distribution, and the Figma example11:01 Getting real feedback as a leader, how to reduce hierarchy and increase ownership14:39 “They need clarity,” decision frameworks over micromanagement18:01 The hidden damage of the founder weighing in on every micro decision20:53 Hiring underrated talent, motivation, ambiguity tolerance, and the stretch role24:38 Why the CEO should invest time in hiring, the leverage math is obviousA line worth keepingThey do not need policies, they need clarity. Pro tips you can steal• Promote leaders who have done the job and set the pace, it earns trust and improves decision quality• Give teams context and constraints, then treat your input like any other input• Use the door test, reversible decisions get speed and delegation, irreversible ones get more diligence• In hiring, look for motivation plus clear thinking, then bet on aptitude over the perfect backgroundCall to actionIf this one helped you think more clearly about leadership and hiring, follow the show and share the episode with one operator who is building under pressure. New conversations drop with different guests and different problems, so you always have something useful to steal.

Arnie Katz has been running product and engineering under one roof since before most companies even considered combining the roles. As CPTO at GoFundMe, he oversees the teams behind a platform processing over 2.5 donations every second, with more than $40 billion in help facilitated worldwide. Arnie breaks down why the CPTO title keeps gaining traction, how he thinks about the role like a portfolio manager, and where the real trade offs live when one person holds both the product and technology reins.Key TakeawaysThe CPTO role works like a portfolio manager. Arnie manages the company's largest investment center by balancing short term business wins against long term platform bets, knowing when to take on technical debt and when to pay it down.Velocity, coordination, and alignment are the three biggest wins. When product and engineering report to one leader, decisions happen faster, roadmap conflicts get resolved without executive tug of war, and technical investments stay tied to business outcomes.The disadvantages are real. Without separate CPO and CTO voices at the executive table, certain perspectives can get muted. His fix: build a leadership bench strong enough to create the right tension underneath him.AI is changing what small teams can deliver. GoFundMe's eight person team behind Giving Funds is shipping at a pace that would have been impossible five years ago.Timestamped Highlights[00:38] The scale most people don't realize about GoFundMe, including 2.5 donations per second and GoFundMe Pro for nonprofits.[02:02] How Arnie first landed the CPTO title at StubHub seven years ago, and why it clicked.[09:11] The real downside of collapsing two C suite roles into one, and how Arnie designs around it.[13:57] His portfolio approach to technical debt, sequencing re platforming in areas like identity and payments while other teams ship business value.[18:38] AI reshaping engineering velocity, the future of the SDLC, and product teams prototyping without writing code.[23:06] Where the CPTO model is headed as the industry evolves.The Line That Stuck"I often think of myself as a portfolio manager. My job is to invest money where the company gets the best returns, where the mission gets the best return, where the shareholder gets the best returns."Pro TipsSequence your bets instead of spreading them thin. GoFundMe gave their identity and payments teams nine months of runway to re platform with no feature expectations while other squads picked up the pace on near term results.Build leadership that creates productive friction. Without CPO vs. CTO tension at the exec level, let your VPs and SVPs push back against each other. That tension is where the best decisions come from.Think in time horizons, not just priorities. Short term moves for 0.1% to 0.5% metric lifts. Midterm bets for 1% to 5% gains. Long term swings that could transform the business. Allocate across all three.If this conversation changed how you think about product and engineering working together, share it with someone on your team. Subscribe to The Tech Trek so you never miss an episode, and connect with Arnie on LinkedIn to keep the conversation going.GoFundMe is offering listeners of The Tech Trek a chance to open their own Giving Fund. For the first 50 people who open a Giving Fund and add $25 or more to their Giving Fund, GoFundMe will add an additional $25 to that Giving Fund. If you have a Giving Fund but have never contributed into it, you can also participate. The deadline for this incentive is March 13. To get this incentive, click here to start your Giving Fund.

Anjali Jameson, Chief Product Officer at Arbiter, says the hard part is not gathering data. It is getting action across patients, providers, and payers without breaking what already works.“Automating something that’s broken is not going to necessarily give us better outcomes.”Arbiter is a care orchestration platform built for patients, providers, and payers together, not a single point solution. The operating spine ingests and makes actionable data across the patient journey, including provider directories, EMR integrations, claims, and financial and policy data from health plans, then connects it to highly personalized multi channel agentic outreach. You will hear why cross system context matters, how total cost of care stays in view while each stakeholder chases different leading metrics, and what it looks like to move from automation into optimization, like going from a call center scheduling flow to 60 percent conversion and pushing toward 95 percent conversion.Timeline00:40 Care orchestration platform, operating spine, data across the patient journey04:33 Misaligned incentives, prior authorizations, 12 to 14 hours a week09:42 Total cost of care, star metric, building for different metrics12:25 Long form personalized videos, transportation, education, medication management15:02 Prior authorization from three to six days to almost instantaneous22:07 COVID, provider messaging two, three X, AI responds fasterSubscribe and share it with someone who is building in health tech.

Most data teams do not have a tooling problem. They have a customer service problem.Mo Villagran, Associate Director of Insights, Analytics, and Data at Cambrex, argues that stakeholder expectation management is the difference between being a trusted advisor and being an order taker."In a simple word, it's really just customer service."In this episode, Mo breaks down how to manage stakeholder expectations, define expected delivery value, and keep projects aligned to real business outcomes instead of chasing rebranded tools. She shares why simple solutions often win, how to show progress even when the work is plumbing, and why qualitative stakeholder testimony beats dashboard count KPIs. You will also hear how she thinks about AI as a tool, when it works, when it is just a cool toy, and how to build trust by demoing in real time.00:02:00 Stakeholder expectation management is customer service00:03:00 Why skeleton teams can still deliver value00:06:00 Who defines expected delivery value, and how to shape it00:09:00 Negotiate expectations, do not become an order taker00:18:00 How to show progress when there is nothing visual00:21:00 Stop chasing quantitative KPIs, win with testimonySubscribe and share this episode with anyone who is knee deep in stakeholder management.

Ashok Krishnamurthi, Managing Partner at Great Point Ventures, says the biggest mistake in venture capital is confusing prediction with judgment.Early stage investing is not about perfect stories, it is about first principles and picking the founder who can execute when the story breaks.This episode is for startup founders and investors who want a cleaner filter for what matters.“You have to learn to check your ego at the door because it’s a partnership.”Ashok shares his path from engineering into building companies, then into venture capital, and explains how he forms an investment thesis when markets are noisy. We talk about founder evaluation, why picking the jockey matters more than the idea, and how first principles thinking shows up in real domains like healthcare data and cancer. We also get practical about artificial intelligence, why AI is not only a compute race, and how AI inference, energy efficiency, and cost shape what wins.00:00 Why legacy matters more than VC metrics02:28 Engineer to founder to venture capital11:16 How to pick the jockey14:21 First principles, cancer data, and AI constraints23:24 AI is here to stay, keep your mind open30:15 How to reach AshokIf this episode helped, subscribe and share it with a builder or investor who will use it.

Aditya Agarwal did not plan to work in robotics. He got rejected from his first-choice major, joined a student club to keep his parents off his back, and stumbled into one of the fastest-growing fields in tech. Now he is Head of Robotics at Medra, a company building physical AI scientists that let researchers run experiments remotely at speeds a traditional lab cannot touch."Even the companies that have made the most progress haven't deployed at the scale of laptops, cars, or phones. So if you have experience scaling hardware products, that is super valuable at an early-stage robotics company."What we get into: why the PhD requirement is mostly gone, how AI is shrinking the hardware development timeline, and the cheapest way to start building with robotics today if you cannot afford to go back to school or take a step back in your career.Timestamped Highlights01:19 The accidental path into robotics that actually worked03:04 Whether you still need an engineering degree for hardware roles04:48 Master's degree vs. early-stage startup: what gets you there faster10:57 How AI is replacing the guesswork in hardware configuration15:51 How to start learning robotics at home without spending much18:38 Why rigid hiring processes are costing robotics teams good candidatesIf this one lands, subscribe and share it with someone who has been thinking about making a move into the space.

Ronak Desai, Co-founder and CPTO at Payment Labs, breaks down a surprisingly hard problem that sits at the intersection of fintech, sports, and compliance. If you have ever assumed paying winners is just a simple payout flow, this episode will change that view fast.Payment Labs helps tournament organizers, league operators, and modern sports businesses handle payouts plus tax compliance and support, all in one system. Ronak explains why spot payments are high risk, why manual workflows still dominate the space, and how stablecoins and AI are about to reshape fraud, identity, and trust.Key TakeawaysOne time payouts are a fraud magnet, inconsistent winners and risk based rules make verification and compliance much harder than payrollSolving payments without solving tax and forms still leaves the biggest liability sitting with the organizerMany sports and esports operators still run payouts in a surprisingly analog way, checks, cash, and post event cleanupAI is now good enough to pressure identity verification, and stablecoins make recovery harder because transfers are effectively finalProduct adoption depends on meeting users where they are, younger athletes expect texting and simple flows, not tickets and portalsTimestamped Highlights00:29 What Payment Labs actually does, payouts plus tax compliance plus support for sports, esports, and creator economy use cases01:15 The origin story, a real tax problem hit an esports operator and exposed how broken the payout workflow is02:46 Why spot payments raise risk, random recipients, fraud pressure, and why bank partners treat this differently than payroll04:58 The industry reality check, still running on checks and cash, and what digitizing the workflow unlocks next06:58 AI fraud versus AI detection, how identity verification is getting bypassed and why stablecoin rails raise the stakes11:55 The NIL wild west and the product lesson, meet athletes where they already live, including iMessage supportA Line Worth RepeatingNow you have AI committing the fraud and then you have AI detecting the fraud.Pro Tips for Builders and OperatorsIf your users are young and mobile first, build support where they already communicate, texting beats ticketing for adoptionDo not bolt on AI for a storyline, use it where it replaces manual work you already do and frees time for higher leverage decisionsMap your tasks with the Eisenhower quadrant, then automate what is repetitive before you chase shiny featuresCall to ActionIf this episode helped you think differently about fintech, fraud, and modern payout infrastructure, follow the show and share it with a founder or operator who touches payments. For more conversations at the intersection of tech, data, and real world execution, connect with Amir on LinkedIn and subscribe to the Elevano newsletter.

Healey Cypher, CEO of BoomPop and COO at Atomic, breaks down what separates founders who win from founders who stall. You will hear a clear way to judge whether an idea is truly worth building, plus the trust mechanics that get investors, customers, and teammates to actually follow you.This conversation is a practical map for tech builders who want to pick smarter problems, execute faster, and earn credibility without the founder theater.Key TakeawaysFounders matter most, but the idea is still a gate, the same great team can get wildly different outcomes depending on the market and timingVC backed is a specific game, it requires not just big potential, but fast scale, and the incentives are not the same as building a profitable lifestyle businessA quick reality check for market size, if you need more than about five to seven percent penetration to hit meaningful revenue, it is usually a brutal pathPainkillers beat vitamins, solve an urgent problem people feel right now, or you risk getting cut the moment budgets tightenTrust is built through authenticity, logic, and empathy, if one wobbles, people feel it fast, and progress slows everywhereTimestamped Highlights00:00:00 Healey’s background, why BoomPop, and what the episode is really about00:02:00 The post pandemic spend shift and the why now behind modern events and group travel00:04:30 Founder versus idea, why execution dominates, but the opportunity still decides the ceiling00:06:40 The VC reality, power law returns, speed, and why some good businesses are still a no for venture00:09:15 A simple market math test, penetration levels that become a growth wall00:19:00 Trust as a founder skill, the three ingredients and how to spot when one is missing00:21:30 Vulnerability as a shortcut to real connection, plus the giver mindset that makes people want you to winA line worth stealingIf everyone wants you to win, it is a lot easier to win.Pro Tips for Tech FoundersAsk yourself what you naturally look forward to doing, that is often your zone of strength, hire around the tasks you dreadLearn the financial basics early, especially cash flow, it is the scoreboard that keeps you alive long enough to winWhen trust is lagging, check the three levers, are you showing the real you, can people follow your reasoning, do they feel you care about their outcomesWhat's next:If you build products, lead teams, or are thinking about starting something, follow the show so you do not miss episodes like this. Also connect with me on LinkedIn for short takeaways and clips from each conversation.

Ty Wang, cofounder and CEO of Angle Health, breaks down what it means to give back through public service, then shows how that same mindset drives his mission to modernize healthcare for small and midsize businesses. We get into why legacy health plans feel opaque and painful, what an AI native health plan actually changes behind the scenes, and how better data and workflows can create real cost stability for employers.Ty shares his path from a federal scholarship and national service work to Palantir, and why he chose one of the most regulated, least glamorous industries to build in. If you have ever wondered why healthcare feels impossible to navigate, or why renewals can blindside a company, this conversation will give you a clear mental model of the problem and a practical view of what modernization looks like when it actually ships. Key TakeawaysHealthcare feels broken because the infrastructure is fragmented, data is siloed, and even basic questions become hard to answer across inconsistent systemsModernizing healthcare is not just about a new app, it is about rebuilding the operational core so workflows, claims, underwriting, and member experience can run on integrated dataSmall and midsize businesses are hit hardest by cost volatility because they lack transparency, predictability, and negotiating leverage, yet health insurance is often a top line item after payrollA strong approach to regulated markets is collaborative, treat regulators as partners in consumer protection, not obstacles to work aroundMission and impact can be a recruiting advantage, especially when the technical problems are genuinely hard and the outcomes touch real people fastTimestamped Highlights00:40 What Angle Health is, and what AI native means in a real health plan02:05 The scholarship path that pulled Ty into public service and set his trajectory04:06 The personal story behind the mission, the American dream, and why access matters09:38 Why healthcare infrastructure is so complex, and how siloed systems create bad experiences11:33 Why SMBs get squeezed, and how manual administration blocks customization at scale13:20 The real pain point for employers, cost volatility and zero predictability before renewal16:55 Why the tech can expand beyond SMBs, but why the SMB market is already massive19:51 Lessons from building in a regulated industry, and why credibility and funding matter22:26 Hiring for high agency, mission driven talent in a world full of AI companiesA line that sticks“Unless you are lucky enough to work for a big company, these modern healthcare services are still largely inaccessible to the vast majority of Americans.”Pro Tips for tech operators and buildersIf you are modernizing a legacy industry, start with the infrastructure layer, fix the data model, integrate the systems, then automate workflowsIn regulated markets, build relationships early, show how your product improves consumer outcomes, and make compliance a design constraint, not a bolt onWhen selling into SMBs, predictability beats perfection, give customers a clear breakdown of what drives costs and what they can controlWhat's next:If this episode helped you see healthcare and legacy modernization more clearly, follow the show on Apple Podcasts or Spotify and subscribe so you do not miss the next conversation. Also, share it with one operator or builder who is trying to modernize a messy industry.

Gabe Ravacci, CTO and co-founder at Internet Backyard, breaks down what the “computer economy” really looks like when you zoom in on data centers, billing, invoicing, and the financial plumbing nobody wants to touch. He shares how a rejected YC application, a finance stint, and a handful of hard lessons pushed him from hardware curiosity to building fintech infrastructure for compute.If you care about where compute is headed, or you are early in your career and trying to find your path without overplanning it, this one will land.Key Takeaways• Startups often happen “by accident” when your competence meets the right problem at the right time• Compute accessibility is not only a chip problem, it is also a finance and operations problem• Rejection can be data, not a verdict, treat it as feedback to sharpen the craft• A real online presence is less about networking and more about being genuinely useful in public• Time blocking and single task focus beats grinding when you are juggling school, work, and a startupTimestamped Highlights00:28 What Internet Backyard is building, fintech infrastructure for data center financial operations01:37 The first startup attempt, cheaper compute via FPGA based prototyping, and why investors passed04:48 The pivot, from hardware tools to a finance informed view of compute and transparency gaps06:55 How Gabe reframed YC rejection, process over outcome, “a tree of failures” that builds skill08:29 Building a digital brand on X, what he posted, how he learned in public, and why it worked13:36 The real balancing act, dropping classes, finishing the degree well, and strict time blocking20:00 Books that shaped his thinking, Siddhartha, The Art of Learning, Finite and Infinite GamesA line worth keeping“The process is really more important than any outcome.”Pro Tips for builders• Treat learning like a skill, ask better questions before you chase better answers• Make focus a system, set blocks, mute distractions, and do one thing at a time• Share what you are learning in public, not to perform, but to be useful and find signalCall to ActionIf this episode sparked an idea, follow or subscribe so you do not miss the next one. Also check out Amir’s newsletter for more conversations at the intersection of people, impact, and technology.

Data leaders are being asked to ship real AI outcomes while the foundations are still messy. In this conversation, Dave Shuman, Chief Data Officer at Precisely, breaks down what actually determines whether AI adoption sticks, from hiring “comb shaped” talent to building trusted data products that make AI outputs believable and usable.If you are building in data, AI, or analytics, this episode is a practical map for what needs to be true before AI can move from demos to dependable, repeatable impact.Key TakeawaysComb shaped talent beats narrow specialization, AI work rewards people who can span multiple skills and collaborate wellAdoption is a trust problem, and trust starts with data integrity, lineage, context, and a semantic layer that business users can understandOpen source drives the innovation, commercialization makes it safe and usable at enterprise scale, especially around security and supportData must be fit for purpose, start every AI project by asking what data it needs, who curates it, and what the known warts areHumans are still the last mile, small workflow choices can make adoption jump, even when the model is already accurateTimestamped Highlights00:56 The shift from T shaped to comb shaped talent, what modern AI teams actually need to look like05:36 Hiring for team fit over “world class” niche skills, and when to bring in trusted partners for depth07:37 How open source sparks the ideas, and why enterprises still need hardened, supported versions to scale11:31 Where AI adoption is today, why summarization is only the beginning, and what unlocks “AI 2.0”13:39 The trust stack for AI, clean integrated data, lineage, context, catalog, semantic layer, then agents19:26 A real adoption lesson from machine learning, and why the human experience decides if the system winsA line worth stealing“You do not just take generative AI and throw it at your chaos of data and expect it to make magic out of it.”Pro Tips for data and AI leadersHire and build teams like Tetris, fill skill voids across the group instead of chasing one perfect profileUse partners for the sharp edges, but require knowledge transfer so your team levels up every engagementMake adoption easier by designing for human behavior, sometimes the smallest workflow tweak beats more accuracyBuild governed data products in a catalog, then validate AI outputs side by side with dashboards to earn trust fastCall to ActionIf this helped you think more clearly about AI adoption, talent, and data foundations, follow the show and turn on notifications so you do not miss the next episode. Also, share it with one data or engineering leader who is trying to get AI out of pilots and into real workflows.